HITRUST CSF requirement statement [?] (New in v11.4.0, coming in Nov. 2024)
The organization formally defines the roles and responsibilities for the
(1) governance,
(2) security, and
(3) risk management
of the organization's deployed AI systems within the organization (e.g., by extending
a pre-existing RACI chart or creating a new one specific to AI).
The organization formally
(4) assigns human accountability for the actions performed by, outputs produced by,
and decisions made by the organization’s deployed AI systems.
- Evaluative elements in this requirement statement [?]
-
1. The organization formally defines the roles and responsibilities for AI governance of the organization's deployed AI systems within the organization (e.g., by extending a pre-existing RACI chart or creating a new one specific to AI).
2. The organization formally defines the roles and responsibilities for AI security of the organization's deployed AI systems within the organization (e.g., by extending a pre-existing RACI chart or creating a new one specific to AI).
3. The organization formally defines the roles and responsibilities for AI risk of the organization's deployed AI systems management within the organization (e.g., by extending a pre-existing RACI chart or creating a new one specific to AI).
4. The organization formally assigns human accountability for the actions performed by, outputs produced by, and decisions made by the organization’s deployed AI systems.
- Illustrative procedures for use during assessments [?]
- Policy: Examine policies related to each evaluative element within the requirement statement. Validate the existence of a written or undocumented policy as defined in the HITRUST scoring rubric.
- Procedure: Examine evidence that written or undocumented procedures exist as defined in the HITRUST scoring rubric. Determine if the procedures and address the operational aspects of how to perform each evaluative element within the requirement statement.
- Implemented: Examine evidence that all evaluative elements within the requirement statement have been implemented as defined in the HITRUST scoring rubric, using a sample based test where possible for each evaluative element. Example test(s):
- For example, review AI systems policy and procedure documentation to determine if roles and responsibilities for the governance, security, and risk management of the organization’s deployed AI systems is in place. Further, confirm that the organization assigns human accountability for the actions performed by, outputs produced by, and decisions made by the organization’s deployed AI systems.
- For example, review AI systems policy and procedure documentation to determine if roles and responsibilities for the governance, security, and risk management of the organization’s deployed AI systems is in place. Further, confirm that the organization assigns human accountability for the actions performed by, outputs produced by, and decisions made by the organization’s deployed AI systems.
- Measured: Examine measurements that formally evaluate and communicate the operation and/or performance of each evaluative element within the requirement statement. Determine the percentage of evaluative elements addressed by the organization’s operational and/or independent measure(s) or metric(s) as defined in the HITRUST scoring rubric. Determine if the measurements include independent and/or operational measure(s) or metric(s) as defined in the HITRUST scoring rubric. Example test(s):
- For example, measures indicate the completeness of the organization’s AI system documentation. Reviews, tests, or audits are completed by the organization to measure the effectiveness of the implemented controls and to confirm that requirements for AI system documentation is maintained.
- For example, measures indicate the completeness of the organization’s AI system documentation. Reviews, tests, or audits are completed by the organization to measure the effectiveness of the implemented controls and to confirm that requirements for AI system documentation is maintained.
- Managed: Examine evidence that a written or undocumented risk treatment process exists, as defined in the HITRUST scoring rubric. Determine the frequency that the risk treatment process was applied to issues identified for each evaluative element within the requirement statement.
- Policy: Examine policies related to each evaluative element within the requirement statement. Validate the existence of a written or undocumented policy as defined in the HITRUST scoring rubric.
- Placement of this requirement in the HITRUST CSF [?]
- Assessment domain: 01 Information Protection Program
- Control category: 02.0 – Human Resources Security
- Control reference: 02.a – Roles and Responsibilities
- Specific to which parts of the overall AI system? [?]
-
- N/A, not AI component-specific
- N/A, not AI component-specific
- Discussed in which authoritative AI security sources? [?]
-
- ISO/IEC 38507:2022- Governance implications of the use of artificial intelligence by organizations
2022, © International Standards Organization (ISO)/International Electrotechnical Commission (IEC)- Where:
- 4. Governance implications of the organizational use of AI > 4.3. Maintaining accountability when introducing AI
- 5. Overview of AI and AI systems > 5.5. Constraints on the use of AI
- 6. Policies to address the use of AI > 6.2. Governance oversight of AI
- 6. Policies to address the use of AI > 6.3. Governance of decision-making
- 6. Policies to address the use of AI > 6.7. Risk > 6.7.3. Objectives
- Where:
- ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system
2023, © International Standards Organization (ISO)/International Electrotechnical Commission (IEC)- Where:
- 5. Leadership > 5.3. Roles, responsibilities, and authorities
- Annex A > A.3. Internal organization > A.3.2. AI roles and responsibilities
- Where:
- OWASP AI Exchange
2024, © The OWASP Foundation- Where: #AIPROGRAM
- Where: #AIPROGRAM
- LLM AI Cybersecurity & Governance Checklist
Feb. 2024, © The OWASP Foundation- Where:
- 3. Checklist > 3.6. Governance > Bullet #1
- 3. Checklist > 3.6. Governance > Bullet #2
- Where:
- Generative AI framework for HM Government
2023, Central Digital and Data Office, UK Government- Where:
- Using generative AI safely and responsibly > Ethics > Accountability and responsibility
- Using generative AI safely and responsibly > Ethics > Accountability and responsibility > Practical recommendations > Bullet 4
- Using generative AI safely and responsibly > Ethics > Accountability and responsibility > Practical recommendations > Bullet 3
- Using generative AI safely and responsibly > Data protection and privacy > Accountability > Practical recommendations > Bullet 1
- Where:
- ISO/IEC 38507:2022- Governance implications of the use of artificial intelligence by organizations
- Helps to prevent, detect, and/or correct which AI security threats? [?]
-
- Denial of AI service
- Prompt injection
- Evasion
- Model inversion
- Model extraction and theft
- Data poisoning
- Model poisoning
- Compromised 3rd-party training datasets
- Compromised 3rd-party models or code
- Confabulation
- Excessive agency
- Sensitive information disclosed in output
- Copyright-infringing output
- Additional information
-
- Q: When will this requirement included in an assessment? [?]
- This requirement will always be added to HITRUST assessments which include the
Cybersecurity for deployed AI systems
regulatory factor. - No other assessment tailoring factors affect this requirement.
- This requirement will always be added to HITRUST assessments which include the
- Q: When will this requirement included in an assessment? [?]
Post your comment on this topic.
Jeremy Huval wrote: Sep 13, 2024
Thanks for the feedback!
Walter Haydock (StackAware) wrote: Sep 12, 2024
I really like the requirement to assign “human accountability for the actions performed by, outputs produced by and decisions made by the organization’s deployed AI systems.” This really drives home the concept of “accountability” which is vaguely discussed in standards like the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF). This phrasing makes clear there must be a designated owner for all AI outputs.